SketchFFusion: Sketch-guided image editing with diffusion model

Sketch-guided image editing aims to achieve local fine-tuning of the image based on the sketch information provided by the user, while maintaining the original status of the unedited areas. Due to the high cost of acquiring human sketches, previous works mostly relied on edge maps as a substitute for sketches, but sketches possess more rich structural information. In this paper, we propose a sketch generation scheme that can preserve the main contours of an image and closely adhere to the actual sketch style drawn by the user. Simultaneously, current image editing methods often face challenges such as image distortion, training cost, and loss of fine details in the sketch. To address these limitations, We propose a conditional diffusion model (SketchFFusion) based on the sketch structure vector. We evaluate the generative performance of our model and demonstrate that it outperforms existing methods.Comment: work in progres

GPPF: A General Perception Pre-training Framework via Sparsely Activated Multi-Task Learning

Pre-training over mixtured multi-task, multi-domain, and multi-modal data remains an open challenge in vision perception pre-training. In this paper, we propose GPPF, a General Perception Pre-training Framework, that pre-trains a task-level dynamic network, which is composed by knowledge "legos" in each layers, on labeled multi-task and multi-domain datasets. By inspecting humans' innate ability to learn in complex environment, we recognize and transfer three critical elements to deep networks: (1) simultaneous exposure to diverse cross-task and cross-domain information in each batch. (2) partitioned knowledge storage in separate lego units driven by knowledge sharing. (3) sparse activation of a subset of lego units for both pre-training and downstream tasks. Noteworthy, the joint training of disparate vision tasks is non-trivial due to their differences in input shapes, loss functions, output formats, data distributions, etc. Therefore, we innovatively develop a plug-and-play multi-task training algorithm, which supports Single Iteration Multiple Tasks (SIMT) concurrently training. SIMT lays the foundation of pre-training with large-scale multi-task multi-domain datasets and is proved essential for stable training in our GPPF experiments. Excitingly, the exhaustive experiments show that, our GPPF-R50 model achieves significant improvements of 2.5-5.8 over a strong baseline of the 8 pre-training tasks in GPPF-15M and harvests a range of SOTAs over the 22 downstream tasks with similar computation budgets. We also validate the generalization ability of GPPF to SOTA vision transformers with consistent improvements. These solid experimental results fully prove the effective knowledge learning, storing, sharing, and transfer provided by our novel GPPF framework.Comment: 22 page

The effect of water temperature on the pathogenicity of decapod iridescent virus 1 (DIV1) in Litopenaeus vannamei

Decapod iridescent virus 1 (DIV1) has caused huge losses to the shrimp breeding industry in recent years as a new shrimp virus. In this study, white leg shrimp, Litopenaeus vannamei, were cultured at different temperatures (26 ± 1 °C and 32 ± 1 °C) and the same salinity, then infected with DIV1 by intramuscular injection to determine the effects of water temperature on viral infection. The DIV1 copy counts in the gills, hepatopancreas, pleopods, intestines, and muscles of L. vannamei were measured in samples collected at 6, 12, and 24 h post-infection (hpi), and the survival rate of L. vannamei was assessed every 6 h after infection. At 96 hpi, the survival rates of L. vannamei in the high (32 ± 1 ℃) and standard (26 ± 1 ℃) water temperature groups were 2.22% and 4.44%, respectively. The peak time of mortality in the high-water temperature group was 6 h earlier than in the standard water temperature group. After 24 hours of DIV1 infection, the DIV1 copy counts in the standard water temperature treatment group were significantly higher than those in the high-water temperature treatment group. The tissues with the highest virus copy counts in the standard and high-temperature groups were the intestines (2.9×1011 copies/g) and muscles (7.0×108 copies/g). The effect of temperature on the pathogenicity of DIV1 differs from that of other previously studied viruses, such as white spot syndrome virus, Taura syndrome virus, and infectious hypodermal and hematopoietic necrosis virus, because the high-water temperature did not mitigate the damage caused by DIV1 infection

A Control and Posture Recognition Strategy for Upper-Limb Rehabilitation of Stroke Patients

At present, the study of upper-limb posture recognition is still in the primary stage; due to the diversity of the objective environment and the complexity of the human body posture, the upper-limb posture has no public dataset. In this paper, an upper extremity data acquisition system is designed, with a three-channel data acquisition mode, collect acceleration signal, and gyroscope signal as sample data. The datasets were preprocessed with deweighting, interpolation, and feature extraction. With the goal of recognizing human posture, experiments with KNN, logistic regression, and random gradient descent algorithms were conducted. In order to verify the superiority of each algorithm, the data window was adjusted to compare the recognition speed, computation time, and accuracy of each classifier. For the problem of improving the accuracy of human posture recognition, a neural network model based on full connectivity is developed. In addition, this paper proposes a finite state machine- (FSM-) based FES control model for controlling the upper limb to perform a range of functional tasks. In the process of constructing the network model, the effects of different hidden layers, activation functions, and optimizers on the recognition rate were experimental for the comparative analysis; the softplus activation function with better recognition performance and the adagrad optimizer are selected. Finally, by comparing the comprehensive recognition accuracy and time efficiency with other classification models, the fully connected neural network is verified in the human posture superiority in identification

Highly Sensitive Fluorescence Probe Based on Functional SBA-15 for Selective Detection of Hg2+

An inorganic–organic hybrid fluorescence chemosensor (DA/SBA-15) was prepared by covalent immobilization of a dansylamide derivative into the channels of mesoporous silica material SBA-15 via (3-aminopropyl)triethoxysilane (APTES) groups. The primary hexagonally ordered mesoporous structure of SBA-15 was preserved after the grafting procedure. Fluorescence characterization shows that the obtained inorganic–organic hybrid composite is highly selective and sensitive to Hg2+ detection, suggesting the possibility for real-time qualitative or quantitative detection of Hg2+ and the convenience for potential application in toxicology and environmental science

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level